A new platform has been utilised to test small molecule drugs. Head of taking the most promising materials for clinical trial. This development follows an expanded collaboration between the AI innovator firm Quris-AI and the German drug giant Merck KGaA.
The inability to predict which drug candidates will work safely and efficaciously in the human body prior to expensive clinical trials continues to create massive challenges for pharmaceutical drug develop¬ment. This is now set to change.
After a two-year validation study, designed to test clinical safety prediction by identifying drug candidate liver toxicity risks, it was announced that Merck KGaA has adopted the startup’s platform.
The preclinical pilot study demonstrated that Quris-AI’s platform could accurately predict which drug caused drug induced liver injury.
Quris-AI’s platform integrates advanced AI, machine learning, generative models, patient-on-chip technology, and proprietary data to predict potential drug-induced toxicities better.
The key points in favour of the technology are:
• An ability to accelerate time-to-market.
• The capability to cut drug development costs.
• Avoiding the potentially harmful pitfalls of traditional animal testing.
In terms of the Merck application, selected preclinical small molecule candidates will be tested through the Quris-AI platform prior to initiating clinical trials.
By pioneering clinical trials on chips the platform is capable of testing thousands of novel drug candidates on hundreds of miniaturized “patients-on-a-chip.” The platform is capable of self-training, resulting in the AI platform accurately predicts clinical safety and efficacy for novel drugs faster and more cost-effectively.
Patient-on-chip technology involves creating miniaturised versions of human organs on microfluidic chips, which can simulate the biological functions of these organs. Not only are reactions improved, the number of tests that can be completed is significant.
One limitation with older organ-chip devices relates to the ability to run detailed experiments. In contrast, the new platform is highly scalable and tightly integrated with the AI, enabling large experiments to be conducted and also at a small fraction of previous costs.
The technology is in keeping with the U.S. FDA Modernization Acts, which seek to push to use AI, organ-on-chip, and other advanced technologies to replace antiquated animal testing.
To deliver these aims, Quris-AI’s drug safety prediction platform integrates advanced AI, machine learning, generative models, patient-on-chip technology, and proprietary data to predict potential drug- induced toxicities better.
The platform is expected to improve safety predictions, speed up time to market, and ultimately enhance success rates.
